Enhanced Hierarchical Fuzzy Formation Control with fuzzy collision avoidance behavior for multiple Mecanum wheeled Mobile Robots

Abstract

Integrating collision avoidance mechanisms into formation control represents a critical aspect for enabling multi-mobile robotic coordination from arbitrary initial configurations. Additionally, the reliance on precise system models for controller design and leader-centralized control architecture limits the flexibility to dynamically reconfigure the formation structure, which is often crucial in real-world applications. Consequently, exploration of modularly interpretable and model-free control strategies emerge as a compelling research direction to address contemporary robotic coordination challenges. This study proposes a Hierarchical Fuzzy Formation Control (HFFC) approach for multiple Mecanum-wheeled Mobile Robots (MMRs) to achieve simultaneous formation tracking, collision avoidance, and orientation alignment. The HFFC leverages a modular hierarchical fuzzy inference system, combining leader–follower and behavior-based strategies. Fuzzified sliding surfaces enhance the tracking performance by minimizing oscillation and chattering effects during formation convergence. Collision avoidance is improved by incorporating inter-MMR approaching rate, enabling proactive anticipation and more responsive maneuvers. A realistic Takagi–Sugeno model, replicating real-world MMR behavior with practical actuator voltage inputs, is developed for evaluation. Simulations demonstrate that five MMRs achieve the desired formation geometry within 1.9 s with 0.048 m accuracy while maintaining a minimum inter-robot distance of 10.77 cm to prevent collisions. Moreover, compared to existing approaches, the proposed control scheme possesses better performance.